Full suppression of viral load is needed to achieve an optimal CD4 cell count response among patients on triple drug antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: To characterize the relationship between plasma viral load (pVL) suppression and triple drug antiretroviral therapy, and the accompanying changes in CD4 cell counts. METHOD: Retrospective study of 465 participants in a HIV/AIDS Treatment Program who initiated triple drug therapy between August 1996 and May 1998. Participants were divided into three groups according to their pVL response: (i) non-responders (NR; n = 112) exhibited pVL persistently > 500 copies/ml over the study period; (ii) partial responders (PR; n = 100) achieved a pVL < 100 copies/ml at least once and subsequently rebounded to > 500 copies/ml; and (iii) full responders (FR; n = 253) achieved a pVL < 500 copies/ml and sustained this level for the remainder of the study period. For each group, the accompanying changes in absolute and fractional CD4 cell counts were evaluated. RESULTS: The median net change in pVL per person from baseline to the end of the observation period was -0.37, -2.27, and -2.56 log10 copies/ml for NR, PR and FR, respectively. During weeks 68-83, the median CD4 cell count (x 10(6) cells/l) was 150 [interquartile range (IQR) 90-370], 380 (IQR 300-480) and 525 (IQR 305-705) for NR, PR and FR, respectively. Median changes in CD4 cells (x 10(6) cells/l) were -20 (IQR -90 to 40), 150 (IQR 30-250) and 240 (IQR 110-365) for NR, PR, and FR, respectively. The net percentage change in CD4 cells per person was 0% (IQR -34-31), 54% (IQR 6-160), and 83% (IQR 39-173) for NR, PR, and FR, respectively. By weeks 68-83, the median fractional CD4 cells was 0.16 (IQR 0.07-0.22), 0.22 (IQR 0.15-0.28), and 0.26 (IQR 0.17-0.34) for NR, PR and FR respectively. CONCLUSIONS: An optimal CD4 cell count response appears to be coupled with continued pVL suppression. Our data indicate that maximal suppression of viral replication should remain the primary goal of therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".